Today, Noemi Derzsy presented a talk “Chicago Crime and House Prices: From Analysis to Prediction” by Noemi Derzsy, Boleslaw Szymanski and Gyorgy Korniss at NetCrime NetSci Satellite Workshop in Indianapolis, IN. The computational tools and the unprecedented amount of real data available today increase interest in study and modeling criminal activities. Here, we propose to build an algorithm to predict housing prices based on the level of criminal activities occurring in neighborhoods. In order to effectively capture the crime rate in community areas, we incorporate in our algorithm the spatio-temporal dynamical aspect of crimes by modeling its spread as an epidemic disease throughout city neighborhoods. Moreover, we analyze the correlation between criminal activities and socio-economic factors, and identify the most influential traits in determining crime rate. We consider these features key attributes for assigning each community area a susceptibility rate of becoming infected at a later time. To forecast house pricing from crime rates, we propose several predictive models, and show that a linear regression based exclusively on crime data from prior years, already shows a strong predictive capability for this purpose. We demonstrate that our forecasting methods can be further improved by integrating social-economic features in the algorithm, as well as a model that captures the dynamics of the crime epidemic in Chicago. We find a strong positive correlation between poverty rate and crime rate in community areas. Additionally, we show that education level, such as studies up to high school degree, show no correlation with crime rates, whereas community areas with higher education at bachelor's degree level exhibit significantly low levels of crime. Similarly, we find a strong negative correlation between the percentage of population owning properties and crime rate.